IN THE UNITED STATES PATENT AND TRADEMARK OFFICE BEFORE THE PATENT TRIAL AND APPEAL BOARD. HULU, LLC Petitioner v.

Transcription

1 IN THE UNITED STATES PATENT AND TRADEMARK OFFICE BEFORE THE PATENT TRIAL AND APPEAL BOARD HULU, LLC Petitioner v. Chinook Licensing DE, LLC Patent Owner Patent No. 7,047,482 PETITION FOR INTER PARTES REVIEW UNDER 35 U.S.C. 311, 37 C.F.R ET SEQ.

2 TABLE OF CONTENTS PETITIONER S LIST OF EXHIBITS...iii II. MANDATORY NOTICES, STANDING, AND FEES... 1 III. OVERVIEW OF CHALLENGE AND RELIEF REQUESTED... 2 A. Publications Relied Upon... 2 B. Grounds for Challenge... 3 IV. Background of the Technology... 4 A. Recommendation Systems... 4 B. The MIT Media Lab in the 1990s... 7 V. OVERVIEW OF THE 482 PATENT... 7 A. Overview of the 482 Patent... 7 B. The 482 Patent Prosecution History... 8 C. The Iron Dome Petition... 8 VI. Claim Construction... 9 A. Level of Skill in the Art B. Contemporaneous Terms C. Directory VII. A REASONABLE LIKELIHOOD EXISTS THAT THE CHALLENGED CLAIMS ARE UNPATENTABLE A. Ground 1: Claims 1-7, and 9-20 are Anticipated Under 35 -i-

3 U.S.C. 102 by Sheth B. Ground 2: Claim 20 Is Obvious Under 35 U.S.C. 103 by Sheth in View of WebCompass C. Ground 3: Claims 1-20 Are Obvious Under 35 U.S.C. 103 by Rucker in View of Sheth VIII. CONCLUSION ii-

4 PETITIONER S LIST OF EXHIBITS Ex. Description 1001 U.S. Patent No. 7,047, File History for U.S. Patent No. 7,047, A Learning Approach to Personalized Information Filtering by Beerud Sheth, Massachusetts Institute of Technology (February 1994) ( Sheth ) 1004 U.S. Patent No. 6,845,374 ( Oliver I ) 1005 U.S. Patent No. 7,158,986 ( Oliver II ) 1006 James Rucker and Marcos Polanco, Siteseer: Personalized Navigation for the Web, COMMUNICATIONS OF THE ACM, Vol. 40 No. 3 (March 1997) ( Rucker ) 1007 Chinook Licensing DE, LLC s Disclosure of Initial Infringement Contentions for the matter of Chinook Licensing DE, LLC v. Hulu, LLC, Case No. 1:14-cv LPS ( Infringement Contentions ) 1008 Declaration of Henry Lieberman ( Lieberman Decl. ) 1009 Scott Wilson, WEBCOMPASS USER GUIDE (Quarterdeck Corp. 1996) 1010 October 10, 2014 Decision Denying Institution of Inter Partes Review for the 482 Patent, IPR Evolving Agents For Personalized Information Filtering by Beerud Sheth and Pattie Maes, IEEE (1993) Amalthaea: Information Filtering and Discovery Using a Multiagent Evolving System by Alexandros G. Moukas, Massachusetts Institute of Technology (June 1997) ( Moukas ) 1013 Complaint for the matter of Chinook Licensing DE, LLC v. Hulu, LLC, Case No. 1:14-cv LPS -iii-

5 Ex. Description 1014 Ed Bott, et al., USING MICROSOFT WINDOWS 95 WITH INTERNET EXPLORER 4.0, Special Ed. (Que Corporation 1998) 1015 Webmate: A Personal Agent for Browsing and Searching, Proceedings for the Second Int l Conference on Autonomous Agents (Katia P. Sycara & Michael Woolridge, eds. 1998) Letizia: An Agent that Assist Web Browsing, Proceedings of the Fourteenth Int l Joint Conference on Artificial Intelligence, Vol. I (Chris S. Mellish, ed. 1995) Iron Dome Petition for Inter Partes Review for the 482 Patent, IPR Declaration of Marilyn McSweeney, Massachusetts Institute of Technology from IPR Library of Congress catalog entry for Using Microsoft Windows 95 with Internet Explorer Library of Congress catalog entry with MARC tags for Using Microsoft Windows 95 with Internet Explorer Library of Congress catalog entry for WebCompass 1022 Library of Congress catalog entry with MARC tags for WebCompass 1023 Discovery of Shared Topics Networks among People A Simple Approach to Find Community Knowledge from WWW Bookmarks by Hideaki Takeda et al., National Institute of Informatics (2000) RFC 1036 Standard for Interchange of USENET Messages 1025 Barton MIT Library Catalog entry with MARC tags of A learning approach to personalized information filtering by Beerud Dilip Sheth 1026 Barton MIT Library Catalog entry with MARC tags of Amalthaea-- -iv-

6 Ex. Description information filtering and discovery using a multiagent evolving system by Alexandros G. Moukas 1027 Scott Wilson, WEBCOMPASS USER GUIDE (Quarterdeck Corp. 1996) (Library of Congress stamped copy) 1028 Marko Balabanovic, Exploring versus Exploiting when Learning User Models for Text Recommendation, USER MODELING AND USER- ADAPTED INTERACTION, vol. 8 at (Feb. 1998) 1029 Learning to Surf: Multiagent Systems for Adaptive Web Page Recommendation, by Marko Balabanovic, Stanford University (1998) -v-

7 Pursuant to 35 U.S.C. 311 and 37 C.F.R et seq., Hulu, LLC ( Petitioner and real party in interest), hereby petitions for inter partes review of claims 1-20 of U.S. Pat. No. 7,047,482 ( the 482 Patent ), issued to Gary Odom. Petitioner hereby asserts there is a reasonable likelihood that at least one of the challenged claims is unpatentable and respectfully requests review of, and judgment against, claims 1-20 as unpatentable under 102 or 103. II. MANDATORY NOTICES, STANDING, AND FEES Real Party in Interest: Petitioner Hulu, LLC is the real party in interest. The 482 Patent was asserted against the Petitioner in Case No. 1:14-cv LPS, Chinook Licensing DE, LLC v. Hulu, LLC, pending in the U.S. District Court for the District of Delaware. See Ex. 1013, Complaint. Related Matters: Case No. 1:14-cv LPS is currently pending in the U.S. District Court for the District of Delaware. The complaint was filed on January 20, 2014 and served on January 27, An independent third-party (Iron Dome LLC) filed a petition for inter partes review of the 482 Patent in April Ex The third-party petition was denied. IPR , Ex Lead Counsel and Request for Authorization: Pursuant to 37 C.F.R. 42.8(b)(3) and 42.10(a), Petitioner designates the following: Lead Counsel is Eliot D. Williams (Reg. No. 50,822) of Baker Botts L.L.P.; Back-up Counsel is Harper S. Batts (Reg. No. 56,160) of Baker Botts L.L.P. -1-

8 Service Information: Service information is as follows: Baker Botts L.L.P., 1001 Page Mill Road, Building One, Suite 200, Palo Alto, CA 94304; Tel. (650) ; Fax (650) Petitioner consents to service by electronic mail at and A Power of Attorney is filed concurrently herewith under 37 C.F.R (b). Certification of Grounds for Standing: Petitioner certifies under 37 C.F.R (a) that the 482 Patent is available for inter partes review. Petitioner is not barred or estopped from requesting inter partes review of any claim of the 482 Patent on the grounds set forth herein. Fees: Under 37 C.F.R (a), the Office is authorized to charge the fee set forth in 37 C.F.R (a) to Deposit Account No , Ref. No , as well as any additional fees due in connection with this Petition. III. OVERVIEW OF CHALLENGE AND RELIEF REQUESTED Petitioner challenges claims 1-20 of the 482 Patent. A. Publications Relied Upon Petitioner relies upon the following patents and publications: Exhibit 1003 A Learning Approach to Personalized Information Filtering by Beerud Dilip Sheth ( Sheth ), published and entered into OCLC (a national bibliographic utility) by the Massachusetts Institute of Technology in 1994, and available as prior art under 35 U.S.C. 102(b). See Ex

9 Sheth was not previously presented to the PTO in the context of the 482 Patent. Exhibit 1006 Siteseer: Personalized Navigation for the Web by James Rucker, et al. ( Rucker ), published at least by March 1997 in vol. 40 of the Communications of the ACM, and available as prior art under 35 U.S.C. 102(b). See Ex , 42. Rucker has not been previously presented to the PTO in the context of the 482 Patent. Exhibit 1009 WebCompass User Guide by Scott Wilson ( WebCompass ), published by at least 1996 and available as prior art under 35 U.S.C. 102(b). Ex , 43. WebCompass has not been previously presented to the PTO in the context of the 482 Patent. B. Grounds for Challenge Petitioner requests cancellation of the claims on the following grounds: GROUND 1: Claims 1-7, 9-20 are anticipated under 102(b) by Sheth. GROUND 2: Claim 20 is obvious under 103 by Sheth in view of WebCompass. GROUND 3: Claims 1-20 are obvious under 103 by Rucker in view of Sheth. Sheth anticipates all elements of claims 1-7 and To the extent Sheth does not expressly or inherently disclose dependent claim 20, Ground 2 provides an additional ground for invalidity for that claim. Ground 3 has been provided as -3-

10 an obviousness combination for dependent claim 8, which is not addressed by Grounds 1 or 2. Ground 3 invalidates all other claims of the 482 Patent as well. IV. BACKGROUND OF THE TECHNOLOGY A. Recommendation Systems The growth of the Internet led to an explosion of research in the 1990s regarding methods for providing relevant information to users out of vast bodies of online information. Ex at 21. Initial development in the 1990s focused on search engines that commonly used keyword analysis. Id. at 22. The next generation of information systems on the Internet included recommendation systems that personalized the information delivered to users according to their individual interests. Id. at 23. Recommendation systems in the mid 1990 s were able to collect information from disparate sources, such as newsgroups, news feeds, web pages, or ontologies. Id. Recommendation systems generated a profile for the user s interests and used that profile to find articles, media, or other types of information that would have been potentially of interest to the user. Id.; Ex at 23. There were multiple ways to generate a user profile. One way to generate a user profile was to do so in real time while the user interacts with the system, for example by actively monitoring the user s actions, or reacting to live user feedback. Ex For example, Henry Lieberman (who provides a declaration supporting this petition) created the Letizia system in

11 with the goal of providing live recommendations to a user while they browsed Web articles. Id. Letizia monitored a user s browsing behavior, updated the profile of the user s interests in real time, and recommended documents based on the user profile while the user was interacting with the system. Id. Another method for generating a user profile was to do so offline, based on the user s past activities or input. Ex For example, offline systems could discern user interests based on the user s browsing history, bookmarks/favorites folders, or past feedback for documents. Id. The user s past activities or input would not be contemporaneous with the generation or updating of the user s profile. Id. To take advantage of the fact that they did not require contemporaneous user input, offline profile generation and updating would often be performed in the background, or scheduled to run during off-peak hours. Id. Off-line profile generation commonly used sample documents for which the user had previously indicated interest to generate user profiles using keyword analysis. Ex at 26; see also Ex at 22; Ex at 9; Ex at 38. Recommendation systems would then use those profiles to search for similar documents based on those keywords. Ex Another source of sample documents was a user s Internet bookmarks or favorites folders. Ex A user s bookmarks or favorites folder is, essentially, a collection of documents in which the user previously expressed interest. Id. The prior art recognized numerous -5-

12 advantages to using bookmarks. See id. at Several recommendation engines generated user profiles based on the user s bookmarks/favorites folders. Ex ; see, e.g., Ex at 1; 1012 at 16; 1023 at 1. For example, Takeda treated bookmark folders as interested topics, and recommended pages were added back to the same bookmark folder. See Ex at 7 (Fig. 4); Ex Moukas used the user s bookmarks as a bootstrapping mechanism for generating a user s profile. See Ex at 16; Ex Because bookmarks represent information the user has previously collected, recommendation engines can utilize bookmarks in background processes without requiring additional user input, or even for the user to be present at all. See Ex at 1. Automated, offline recommendation systems were disclosed by numerous prior art patents, including several issued to Mailfrontier, Inc. Ex For example, U.S. Patent No. 6,845,374 to Oliver et al. disclosed a system that was invoked automatically by a software program to develop a recommended set for existing clients not currently logged on. Ex at 2: The system assembled pre-existing user data from numerous sources, such as shopping history, , and prior Internet searches, into an interest set. See, e.g., Ex at 6:4-20, Fig. 3, 4. Keywords were extracted from the interest set, and those keywords were used to recommend similar documents. See, e.g., Ex at 6:4-20, Fig. 3, 4, 8, and 9. Similarly, U.S. Patent No. 7,158,986 to Oliver et al. disclosed the use -6-

13 of an interest folder for collecting information about the user s interests, such as the history of the user s Internet viewing, recommendations for the user, [or] a summary of the user s purchases. See Ex at 14: Recommendation software used the user s interest folder to identify similar items of interest, which were written back to the same interest folder. See id. at 14:66-15:7, Figs B. The MIT Media Lab in the 1990s The MIT Media Lab conducted prolific research into the field of Human- Computer Interaction (HCI) and the subfield of Intelligent User Interfaces (IUI). Ex A number of graduate students came through the MIT Media Lab in the 1990s, including Beerud Sheth, the author of the Sheth reference, and Alex Moukas, whose thesis was a follow-up to Sheth that added, among things, new autonomous agent algorithms and the ability to build a user profile using the user s bookmarks folder or browser history. Ex ; Ex at 16, 26, 31. V. OVERVIEW OF THE 482 PATENT A. Overview of the 482 Patent The 482 Patent was filed February 28, The 482 Patent is directed to offline information filtering. The alleged invention automatically finds, saves, and displays links to documents topically related to a set of documents without a user having to search or specify search terms. Ex at 2: During prosecution, the applicant repeatedly differentiated the prior art as interactive user searching, as opposed to the alleged automatic search of the alleged invention. -7-

14 See, e.g., Ex at 219; Ex B. The 482 Patent Prosecution History The Examiner initially rejected all pending claims (i.e., pending claims 9-25) based on several prior art references. The applicant traversed the rejections, but the Examiner issued a final rejection of all pending claims. Applicant appealed on February 18, 2005, but the Board held the appeal brief was defective. After the undocketed appeal was returned to the Examiner, the applicant filed a request for continued examination and amended all claims to include elements requiring accessing [a document] without contemporaneous user selection and searching additional documents without contemporaneous user input of a search location. Ex at A notice of allowance issued one month later. Ex at 18. C. The Iron Dome Petition Independent third-party Iron Dome LLC filed a petition for inter partes review of the 482 Patent. Ex ( the Iron Dome Petition ). That petition was denied because it failed to address the limitation of accessing [a document] without contemporaneous user selection. Ex at The Iron Dome petition relied on two prior art references: a paper by Liren Chen ( Chen, Ex. 1015) and a paper by Henry Lieberman ( Lieberman, Ex. 1016). Both references monitored the user s web browsing while user was interacting with the system, and both generated profiles for the user in real-time. Ex Lieberman -8-

15 disclosed the Letizia system, which was discussed above. See supra, Section IV( A). Letizia monitored the user s web browsing and provided the user with live recommendations while they surfed the Web. Ex at 24, 56. Similarly, Chen disclosed a proxy server and an applet controller to monitor a user s browsing and searching activities and learn from them. Id.; Ex at 11. Chen updated the profile in real-time, whenever a user indicated that they liked a particular document. Ex at 24, 56; Ex at 12. In contrast to the real-time systems from the Iron Dome Petition, the prior art references relied on here use off-line profile generation to target a usage model that does not depend on user interaction. Ex These off-line references discern user interests using past user feedback or already-existing bookmarks. Id. Because the prior art relied on in this petition operated without user interaction, those references clearly satisfy the elements of the 482 Patent relating to accessing [a document] without contemporaneous user selection [or input]. Id. VI. CLAIM CONSTRUCTION Pursuant to (b), and solely for purposes of this review, Petitioner construes the claim language such that claim terms are given their broadest reasonable interpretation ( BRI ). For terms not specifically listed and construed below, and in the absence, to date, of detailed arguments from the Applicant indicating a need for construction or a disagreement regarding the -9-

16 meaning of the vast majority of terms, Petitioner interprets them for purposes of this review in accordance with their plain and ordinary meaning under the required BRI standard. Because this standard is different from the standard used in U.S. District Court litigation (see In re Am. Acad. of Sci. Tech Ctr., 367 F.3d 1359, 1364, 1369 (Fed. Cir. 2004); see also MPEP 2111), Petitioner expressly reserves the right to argue a different claim construction in litigation. A. Level of Skill in the Art The art at issue in the 482 Patent relates to the field of Human-Computer Interaction ( HCI, also referred to as Human-Computer Interface), which includes information filtering and information retrieval. See, e.g., Ex , 48; Ex at 23 ( User modeling can be defined as the effort to create a profile of the user s interersts [sic] and habits and employ the profile in order to improve humancomputer interaction. ). One in the art would be considered to possess ordinary skill in the art with a Bachelor of Science in Computer Science, Electrical Engineering, or Computer Engineering, or a similar amount of coursework. 1 Ex One of ordinary skill in the art would also have at least two years of experience either working in the field or in post-secondary education. Id. This level of education and work experience would be necessary to read and understand the 1 A Master of Science or Ph.D. in these fields would also have been sufficient. -10-

17 482 Patent. Id. Someone with less technical education but more experience also meets this standard. B. Contemporaneous Terms Claims 1, 11, and 16 include various terms related to contemporaneous user selection of a document or contemporaneous user input of a search location. The Board has previously construed accessing [a document] without contemporaneous user selection as accessing a document, but not at the same time or period that a user selects that document. See Ex at 7. C. Directory The term directory is used numerous times in the claims of the 482 Patent. It appears in claims 1, 2, 5, 6, 8-11, 15, 16, 19, 20 in the elements augmenting a directory, first directory, and results directory. The specification for the 482 Patent states, As depicted in FIG. 2, a directory 3, if not empty, comprises a set of documents 2, or a set of links 1 to documents 2, or a combination of documents 2 and links 1. Ex at 3: In district court, the Patent Owner advanced a broad construction for directory, asserting that the user s viewing history, which includes multiple documents, is a directory. Ex at 9. In addition, the Patent Owner argued that [a] recommendation carousel is a directory, referring to graphical, sliding carousels (i.e. rows of videos) in the web page for Hulu s Recommendation Hub. Ex at 6, 49, 77. Thus, according -11-

18 to the Patent Owner, the BRI of directory in the context of the 482 Patent would include a collection of documents and/or links to documents. Ex VII. A REASONABLE LIKELIHOOD EXISTS THAT THE CHALLENGED CLAIMS ARE UNPATENTABLE. Pursuant to and (b), all of the challenged claims are unpatentable for the reasons set forth in detail below. A. Ground 1: Claims 1-7, and 9-20 are Anticipated Under 35 U.S.C. 102 by Sheth. Sheth discloses a system that finds articles which match the [user] profiles. Ex at 32. One method for creating a profile is to provide examples of articles that the agent did not retrieve, which is an example of programming by demonstration. Id. at 26. By showing examples of interesting articles, the user is indirectly programming the agent to get good articles in the future. Id. at 37. Programming by demonstration is particularly useful while training a new agent. Id. When the user gives feedback by providing an example article of interest, the article is not immediately analyzed instead, a link to that article is added to the ArtFeedback directory. See, e.g., Ex at 46 ( The profile maintains a list of articles for which feedback was received, if any, and the amount of feedback for each, during the last user session. This can be seen in the items listed under ArtFeedback in table 4.1. ), 40 ( The solution adopted in this implementation is that the pointers of documents are stored when feedback is provided, but the -12-

19 profiles are not immediately modified. ); Ex Every night, the system accesses the articles listed in the ArtFeedback directory, updates user profiles with keywords from those articles, and searches for similar articles matching those keywords/profiles. See Ex at 17-18, 40-46; Ex Links to topscoring articles are added to a directory in the profile and then presented to the user the next day. See id. Claim 1 1[p]. A computerimplemented method for augmenting a directory without contemporaneous user input comprising: Sheth See, e.g., 20: An information filtering agent assists the user with the task of finding interesting news articles in a particular domain. See, e.g., 17: The proposed idea is to build a set of adaptive autonomous interface agents that inhabit the user s computer and are assigned the goal of being responsive to the information needs of the user. The agents can sense user feedback as well as changes in the information environment. The agents are autonomous as they can take actions relating to news filtering on the user s behalf. See, e.g., 46: Finally, YAIF writes back the updated profile as well as pointers to filtered articles into the profile. See, e.g., 46: Only the pointers to the selected articles are stored by YAIF in the profile, as in the ArtScore list in table 4.1. The article is not stored but is retrieved upon user request. See, e.g., 42: YAIF is a time-intensive process and is run offline. The process is executed every night, so that filtered articles are available to every profile in the morning. -13-

20 As explained below with respect to element 1[f], a profile contains an ArtScores directory for results. See Ex at 41, 46. Thus, when the system finds new recommended articles and writes pointers to the filtered articles into the profile (Ex at 46), it satisfies the augmenting a directory element (Ex Because [t]he Information Filtering module (called YAIF, for Yet Another Information Filter) runs offline, every night (Ex at 42), it runs without contemporaneous user input (Ex ). The automated interface agents of Sheth operate totally autonomously. Ex at Claim 1 1[a] accessing at least a first document via a first directory without contemporaneous user selection of said first document, said first document comprising at least in part topical textual content; Sheth See, e.g., 40: The list of items labeled 'ArtFeedback' in table 4.1 is the list of pointers to articles which received feedback in the last user session. Next to the pointers is a positive or negative number indicating the feedback for the article. The next time the Information Filtering module filters articles, it re-creates the document representation for all articles and the profile is then modified. See, e.g., 42: YAIF is a time-intensive process and is run offline. The process is executed every night, so that filtered articles are available to every profile in the morning. See, e.g., 41: See, e.g., 21: Since the terms are not all equally important for content representation, importance factors (or weights) are assigned to the terms in proportion to their presumed importance for text content identification -14-

21 ArtFeedback is a collection of pointers (links) to articles and thus satisfies the BRI of first directory. Ex When the Information Filtering module runs, it accesses the articles linked to in ArtFeedback (i.e., at least a first document). Ex at 40; see also Ex As explained above, the Information Filtering module (YAIF) runs offline, meaning [t]he process is executed every night (Ex at 42), and thus the Information Filtering module accesses the articles linked to in ArtFeedback without contemporaneous user selection (Ex ). The articles contain topical textual content as explained below, the articles contain keywords used for content representation based on their presumed importance for text content identification. Ex at 21. The articles are newsgroup articles (id. at 41-44), and a person of ordinary skill in the art would recognize that a newsgroup article contains topical textual content (Ex ). Thus Sheth discloses all limitations in element 1[b]. See id. Sheth discloses that users can give feedback by providing an example article via programming by demonstration. Ex at 26. The system adds a link to the article to the ArtFeedback directory to be processed later, at night. See Id. at 46 ( The profile maintains a list of articles for which feedback was received, if any, and the amount of feedback for each, during the last user session. This can be seen in the items listed under ArtFeedback in table 4.1. ); id.at 40: [T]he document representation is generated while scoring the document -15-

22 and is not stored thereafter. Therefore, when feedback for a retrieved document is provided, its representation is no longer available for modifying the profile. This problem is much more acute in programming by demonstration, where feedback is provided for an article for which no representation has yet been created. The solution adopted in this implementation is that the pointers of documents are stored when feedback is provided, but the profiles are not immediately modified. The list of items labeled 'ArtFeedback' in table 4.1 is the list of pointers to articles which received feedback in the last user session. See also Ex The next time the Information Filtering module filters articles (at night without contemporaneous user selection), it accesses and recreates the document representation for documents listed in ArtFeedback, which includes the example article (i.e., the first document) provided via programming by demonstration. See Ex at 40; Ex Claim 1 1[b] deriving at least one keyword indicative of at least one topical content from said first document; Sheth See, e.g., 23: The term-vector for the keyword field is obtained through a full text analysis of the documents. The weight of the term depends on its frequency of occurrence in the text and the number of documents it appears in. (emphasis added) See, e.g., 23: Documents are represented as a set of fields where each field is a term-vector (see Section 3.1.2). The fields of the document representation must be extracted from the document itself.... The keyword field is generated from the text of the article. See, e.g., 41 (Table 4.1: A sample interest profile): keywords:

23 ukraine nuclear additional returned washington See, e.g., 21: Since the terms are not all equally important for content representation, importance factors (or weights) are assigned to the terms in proportion to their presumed importance for text content identification As explained with element 1[a], when the Information Filtering module (YAIF) runs at night, it re-creates the document representation for articles in the ArtFeedback directory, which would include the claimed first document. Ex at 40; see also Ex The document representation includes a keyword field with a vector of keywords obtained through a full text analysis of the documents. Ex at 23; see also Ex Thus, YAIF derives at least one keyword from said first document. Ex That keyword is indicative of at least one topical content from the first document. Id. Sheth explains that keyword terms are used for content representation and weights are assigned to the terms in proportion to their presumed importance for text content identification. Ex at 21. A POSITA would understand that keywords derived from a document are indicative of at least one topical content from that document. Ex The document representation, including its keyword field, are added to the -17-

24 interest profile. See Ex at 40-41; Ex Table 4.1 of Sheth shows keywords derived from documents and stored in a profile. See id. Profiles and documents both contain keyword vectors. See Ex at 20, 22; Ex When the user creates a new profile by providing a sample document, the profile contains a list of keywords derived exclusively from the example document (i.e., the first document). Ex When programming by demonstration is used to create a new profile, the system creates a profile which looks like D [the example Document provided by the user]. Ex at 27; see also Ex In that instance, the keyword vector in a profile, as illustrated by Table 4.1, would contain more than one keyword indicative of the topical content from the sample document (first document) provided by the user. Ex Claim 1 1[c] searching as a background operation a plurality of documents in storage in at least one computer without contemporaneous user input of a search location, Sheth See, e.g., 42-44: For every profile, YAIF retrieves each article from the two sets of newsgroups and scores them with respect to the profile. See, e.g., 20: An agent is modeled as a population of profile individuals, each of which searches for articles in a small domain.... The profile contains information about where to search for articles and what kinds of articles to filter. See, e.g., 42: The Information Filtering module (called YAIF, for Yet Another Information Filter) is responsible for actually retrieving articles from the database of news articles. YAIF takes the profiles, scores articles with respect to the profiles and selects the high scoring articles to be presented to the user. YAIF is a time-intensive process and is run offline. The process is executed every -18-

25 night, so that filtered articles are available to every profile in the morning. YAIF searches newsgroup articles (i.e, a plurality of documents in storage). Ex at 42-44; see also Ex The information filtering module YAIF runs offline, at night (i.e., as a background operation). Ex at 42; see also Ex Thus it searches a plurality of documents without contemporaneous user input of a search location. See Ex at 20 ( The profile contains information about where to search for articles[.] ); Ex Newsgroup articles are stored on USENET servers (i.e., at least one computer). See Ex at 42 ( On typical USENET servers, the set of articles in a newsgroup changes on a continual basis. ); Ex Claim 1 1[d] such that said search comprises searching for documents related by said at least one keyword to said first document, thereby accessing a second document; Sheth See, e.g., 23: The keyword field is generated from the text of the article. See, e.g., 17-18: The profiles used by the filtering system consist of terms which are matched with the contents of the documents... Each profile searches for documents that match itself and recommends them to the user. See, e.g., 22: The filtering process consists of translating documents to their vector space representations, finding documents which are similar to the profiles and selecting the top-scoring articles for presentation to the user. See, e.g., 42-44: For every profile, YAIF retrieves each article from the two sets of newsgroups and scores them with respect to the profile. -19-

26 1[e] determining relevance of said second document to said at least one keyword; and See, e.g., 24 (emphasis added): The similarity between a document and a profile is a function of the similarities between the corresponding fields. The field similarities are first computed. Since each field is a term vector, the metric used for measuring similarity between two fields of the same type is just as in equation 3.1. The similarity scores for the corresponding fields in the document and the profile are computed as shown in equation 3.2. The similarity between a complete document and complete profile is computed next. It is the sum of the field similarity scores weighted by field weights of the profile. See supra, element 1[d]. The Information Filtering module YAIF searches for articles that are similar to the user profile. Ex at 42; Ex The profile contains a keyword vector including more than one keyword from the first document (the example article provided by the user. See Ex at 20, 22; Ex ; supra element 1[b]. The representation of a profile is similar to that of a document. Ex at 22. The advantage of using a common vector space for both documents and queries [i.e. profiles] is that a document can also be used as a query itself i.e., one can find documents that are similar to a given document. Id. at 21 (emphasis added). When YAIF searches for documents similar to a profile, it searches for documents related by at least one keyword in the keyword vector. Ex When programming by demonstration is used to create a new profile, the -20-

27 system creates a profile which looks like D [the example document provided by the user]. Ex at 27; Ex , 69. The profile will thus contain keywords from the example document (i.e., the claimed first document). Ex , 69. YAIF will score articles based on their similarity to the profile, which is includes measuring the similarities between the keyword vectors. Ex at 24-25; Ex Thus, when YAIF searches for top-scoring articles, it searches for documents similar to (i.e., related) by at least one keyword (as stored in the profile) to the example document provided by the user (i.e., the first document). Ex Regarding element 1[e], a person of ordinary skill in the art would recognize that the similarity score is indicative of the relevance of the article. Ex As explained for element 1[d], YAIF calculates a similarity score (i.e., determines the relevance) that includes a calculation of the article s keyword vector and keyword vector in the profile. Ex at 24-25; Ex The keyword vector of the profile includes at least one keyword derived from the first document. See supra, element 1[b]. Thus, YAIF calculates the similarity (i.e., relevance) of searched articles (including the second document) to at least one keyword derived from the first document. Ex Claim 1 1[f] adding a reference to said second document in a results Sheth See, e.g., 46: Only the pointers to the selected articles are stored by YAIF in the profile, as in the ArtScore list in table 4.1. The article is not stored but is retrieved upon -21-

28 directory. user request. See, e.g., 41 Table 4.1 (sample interest profile).: Sheth further elaborates that YAIF writes back the updated profile as well as pointers to filtered articles into the profile. Ex at 46. ArtScores is a directory in the profile: it is a collection of pointers (i.e., links) to recommended/filtered articles. See Ex at 41, 46; Ex Thus, at least one pointer (i.e., a reference) to a recommended article (i.e., the second document) is stored in the ArtScores directory (i.e., the results directory). Ex A POSITA would understand that pointers, including the pointers to newsgroup articles shown in Table 4.1, are references. Ex The ArtScores directory is later displayed to the user as required by the additional step[s] of claims 6 and 9 and explained in those respective sections. Id. Claim 2 2. The method according to claim 1, wherein at least part of said storage is on a different computer than the computer storing said first directory. Sheth See, e.g., 42-44: For every profile, YAIF retrieves each article from the two sets of newsgroups and scores them with respect to the profile. See, e.g., 42: On typical USENET servers, the set of articles in a newsgroup changes on a continual basis. See, e.g., 46: Only the pointers to the selected articles are stored by YAIF in the profile, as in the ArtScore list in table 4.1. The article is not stored but is retrieved upon user request. -22-

29 The Information Filtering module (YAIF) retrieves newsgroup articles from USENET servers. See Ex at 42; Ex Those servers are different computers than the one running YAIF. Ex Sheth explains that YAIF only stores pointers to selected articles; it only retrieves those articles from the USENET servers (the different computer) upon user request. See Ex at 46. Claim 3 3. The method according to claim 1, further comprising deriving a plurality of keywords. Claim 4 4. The method according to claim 3, further comprising ranking at least two of said plurality of keywords. Sheth See, e.g., 21: A text is then representable as a vector of terms T i =< w ij > where wij represents the weight of term t j in text T i. See supra, element 1[b]. Sheth See, e.g., 23: The weight of a keyword-term is the product of its term frequency and its inverse document frequency. See supra, element 1[b] and claim 3. With respect to claim 3, Sheth discloses a vector of keyword terms as explained above for element 1[b]. The keyword vector includes more than one keyword, and thus includes a plurality of keywords as shown in Table 4.1 (charted for element 1[b]). See also Ex Specifically, the vector of keyword terms is described in the equation T i =< w ij > which discloses a vector of j terms (i.e., a plurality of keywords) for the text T i. Ex at 21. With programming by demonstration, some or all of the keywords in that vector are derived from the sample article (the first document) provided by the user. Ex ,

30 Regarding claim 4, keyword terms are not all equally important for content representation, and thus each keyword term is assigned a weight. Ex at 21. The weight of a keyword-term is the product of its term frequency and its inverse document frequency. Id. at 23. The weight of a keyword reflects its importance. Id. at 21 ( [I]mportance factors (or weights) are assigned to the terms ). As explained in element 1[b] and shown in Table 4.1, the keywords terms in a profile are sorted by weight (i.e. ranked) from highest to lowest. Id. at 41; see also Ex Thus at least two of the plurality of keywords are ranked. Ex Claim 5 5. The method according to claim 1, further comprising accessing a plurality of documents in said first directory. Sheth See supra, element 1[a]. As shown for element 1[a], the ArtFeedback directory (the first directory), contains a list of articles (a plurality of documents) that are accessed to create document representations when YAIF is run nightly. See also, Ex Claim 6 6. The method according to claim 1, with the additional step of signifying the relevance of said second document to documents in the first directory when displaying said results directory. Sheth See, e.g., 35 (emphasis added): Clicking on an Agent Icon brings up a window (News Window) for browsing through the news articles retrieved by that agent (see figure 4-2). In the left half of the News Window are the titles of the articles selected by the agent. The color code of the agent which retrieved the articles, is used as the background color for the list of titles. To the left of to [sic] each document title is a small bar graph indicating the score assigned to it by the agent. The titles are sorted in decreasing order of these scores. See, e.g., 34: -24-

31 See, e.g., 24-25: When the agent collects all the topscoring articles retrieved by the profiles, it is not possible to compare the scores unless they are all on the same scale. Besides, a user would not be able to make sense of the similarity score if the scale is not known. Hence, document scores are constrained to be in the closed interval [-1, 1]. The highest score of 1 would only be assigned when the document and profile representations are identical. As shown in Figure 4-2, the small bars on the left of the recommended article titles are bar graphs indicating the similarity score with the profile (i.e., signifying the relevance). See Ex at 34-35; Ex When using programming by example to create a new profile, the profile will reflect contents and keywords of the example articles provided by the user. See Ex at 27; Ex , 77. Example articles provided by the user are added to the ArtFeedback directory for processing at night. Ex at 40; Ex , 77. Therefore, the displayed similarity scores will reflect the relevance of each article (i.e., the second document) to the example articles (i.e., the documents) -25-

32 listed in the ArtFeedback Directory (i.e., the first directory). Ex Claim 7 7. The method according to claim 1, with the additional step of comparing the relevance of said second document to a preset threshold. Claim 9 9. The method according to claim 1, with the additional step of displaying said results directory. Claim The method according to claim 1, further comprising recognizing a precondition for autonomously augmenting said results directory, prior to accessing said first document. Sheth See, e.g., 25: A third approach is to use a threshold. Any document which scores above the threshold will be selected independent of the profile which scored it. See, e.g., 65 (describing a test scenario): An arbitrary threshold (0.05) for document scores is set. Only documents scoring above the the threshold are retrieved for presentation. To achieve the desired goal of high recall and high precision the similarity scores of relevant documents must lie above the threshold and the scores of irrelevant documents must lie below the threshold. Sheth See, e.g., 20: The articles recommended by each of the profiles are collected together and presented to the user. See supra, claim 6. Sheth See, e.g., 42: The Information Filtering module (called YAIF, for Yet Another Information Filter) is responsible for actually retrieving articles from the database of news articles. YAIF takes the profiles, scores articles with respect to the profiles and selects the high scoring articles to be presented to the user. YAIF is a time-intensive process and is run offline. The process is executed every night, so that filtered articles are available to every profile in the morning. See, e.g., 16: Autonomous adaptive agents operate totally autonomously and become better over time at achieving its goals. See, e.g., 46: Only the pointers to the selected articles are stored by YAIF in the profile, as in the ArtScore list in table 4.1. Information filtering module YAIF runs every night. Ex at 42. Thus, -26-

33 the system recognizes a precondition (e.g., nightime or a specific time trigger such as midnight) that initiates YAIF. Ex After YAIF begins to run, it accesses the articles (including the first document) in ArtFeedback (i.e., the first directory). See Ex at 42; Ex YAIF s autonomous agents augment the ArtScores directory (i.e., the results directory) with new recommendations. See Ex at 46; Ex at 81. Claim 11 11[p]. A computer-implemented method for augmenting a directory comprising: 11[a] autonomously initiating operation based upon a stored precondition; 11[b] accessing at least a first document without contemporaneous user selection, wherein said first document comprises at least in part topical textual content; 11[c] deriving at least one keyword indicative of at least one topical content within said first document; 11[d] as a background operation, searching in storage in at least one computer for documents related by said at least one keyword to said first document, 11[e] wherein at least some of said searched documents are independent and not organized in relation to one another; Sheth See supra, element 1[p]. See supra, claim 10. See supra 1[a]. See supra 1[b]. See supra 1[c] and 1[d]. See, e.g., 42-44: For every profile, YAIF retrieves each article from the two sets of newsgroups and scores them with respect to the profile. See, e.g., 41 (Table 4.1: A sample interest profile): newsgroups: 0.2 clari.news.cast 1 clari.news.gov.international 1 clari.news.hot.easteurope 1 clari.news.hot.ussr 1 clari.news.top.world 1-27-

34 USENET is a distributed Internet discussion system. Ex Newsgroups articles are independent and not organized in relation to one another because articles from one newsgroup are not organized in relation to articles from other newsgroups. Id. Sheth teaches retrieving articles from multiple newsgroups for each profile, as shown in Table 4.1. Ex at 42-43; Ex Even within a newsgroup, articles were not organized in terms of content anyone could post to a newsgroup at any time, and newsgroups were often full of spam. Ex Therefore, at least some of the searched newsgroup articles (said searched documents) are independent and not organized in relation to one another. Id. Claim 11 11[f] determining relevance of a search-accessed second document to said at least one keyword; and 11[g] adding a reference to said second document in a results directory. Sheth See supra, element 1[e]. See supra, element 1[f]. Claim The method according to claim 11, wherein said storage is on a plurality of computers connected to at least one network. Sheth See, e.g., 42-44: For every profile, YAIF retrieves each article from the two sets of newsgroups and scores them with respect to the profile. See, e.g., 42: On typical USENET servers, the set of articles in a newsgroup changes on a continual basis. See, e.g., 46: Only the pointers to the selected articles are stored by YAIF in the profile, as in the ArtScore list in table 4.1. The article is not stored but is retrieved upon user request. USENET is a distributed Internet discussion system with no central server; -28-

35 instead, USENET is distributed among a large conglomeration of servers that store and forward messages to one another in so-called news feeds. Ex Therefore, USENET news feeds are stored on a plurality of servers (i.e., computers) connected to the Internet (i.e., at least one network). Id. Claim 13 Sheth 13[p]. The method according to claim 11, See supra, claim 11. further comprising: 13[a] deriving a plurality of keywords; and See supra, claim 3. 13[b] determining relevance of said second See supra 1[e]. document to said plurality of keywords. Claim 14 Sheth 14. The method according to claim 11, See supra, claim 7. further comprising comparing the relevance of said second document to a preset threshold. Claim 15 Sheth 15. The method according to claim 11, See, e.g., 46: While selecting further comprising conditionally adding articles, care is taken to prevent said reference to said second document presenting the same article twice in depending upon whether said reference to different sessions. The profile keeps said second document already exists in said a list of articles that have been results directory. presented to the user during previous sessions, as in the ArtRead list in table 4.1. The top scoring articles are compared to this list to prevent repetitions. Claim 16 Sheth 16[p]. A computer-implemented method See supra, element 1[p]. for augmenting a directory comprising: 16[a] accessing a plurality of grouped See supra, element 1[a]. documents without contemporaneous user selection initiating said access; As shown for element 1[a], the ArtFeedback directory in a profile is a -29-

36 collection of links to articles (i.e., the plurality of grouped documents) that are accessed to create document representations when YAIF is run offline at night (i.e., without contemporaneous user access). See also Ex Claim 16 16[b] deriving a plurality of keywords indicative of an aggregate content of said grouped documents; Sheth See, e.g., 23: The term-vector for the keyword field is obtained through a full text analysis of the documents. The weight of the term depends on its frequency of occurrence in the text and the number of documents it appears in. See, e.g., 23: Documents are represented as a set of fields where each field is a term-vector (see Section 3.1.2). The fields of the document representation must be extracted from the document itself.... The keyword field is generated from the text of the article. See, e.g., 46: The profile maintains a list of articles for which feedback was received, if any, and the amount of feedback for each, during the last user session. This can be seen in the items listed under ArtFeedback in table 4.1. See, e.g., 57: Initially, when the profile is empty, the articles retrieved are in a random order.... In that case, the user needs to program the agent by demonstration by providing a few examples of relevant documents. After feedback is provided to the relevant articles, the profile is modified and the search is performed again. See, e.g., 27: The resulting effect is that, for those terms already present in the profile, the term-weights are modified in proportion to the feedback. Terms not already in the profile are added to the profile. See, e.g., 56: -30-

37 See, e.g., 21: Since the terms are not all equally important for content representation, importance factors (or weights) are assigned to the terms in proportion to their presumed importance for text content identification See supra, claim 3. Sheth discloses Scenario 1 where [t]he initial profile is nearly empty and is shown on the left hand side of Table 5.1. Ex at 54. Sheth explains that, because the profile is empy, the user needs to program the agent by demonstration by providing a few examples of relevant documents. Id. at 57. When the user provides feedback for documents or provides example documents via programming by demonstration, pointers to those documents are added to (i.e., -31-

38 grouped together in) the ArtFeedback directory. See id. at 40, 46; Ex ; see supra, element 1[a]. The effect of this feedback is shown in Table 5.1 (titled Static user interests: effect of feedback ) where the right-hand side shows the final profile including a cumulative keyword vector derived from (i.e., indicative of the aggregate content of) the example documents provided by the user (the grouped documents). See Ex at 56; Ex Because the initial profile is empty, the profile after one day after the user provided feedback and example documents for programming by demonstration is a keyword vector (a plurality of keywords) with terms derived exclusively from the set of documents in the ArtFeedback directory (the grouped documents). Ex When feedback for a document is provided for an empty profile, it effectively creates a profile which looks like [the document] D. Id. at 27. After the first document in ArtFeedback is analyzed, feedback for the remaining documents in ArtFeedback are added to the profile. Ex The resulting effect is that, for those terms already present in the profile, the termweights are modified in proportion to the feedback. Terms not already in the profile are added to the profile. Ex at 27; Ex Thus, the list of keyword terms in the profile are indicative of the aggregate content of the documents grouped together in the ArtFeedback directory. Ex Claim 16 16[c] prioritizing a Sheth See, e.g., 21: Generally speaking, a term is used for -32-

39 relative relevance of said keywords; text identification. Since the terms are not all equally important for content representation, importance factors (or weights) are assigned to the terms in proportion to their presumed importance for text content identification See, e.g., 23: The term-vector for the keyword field is obtained through a full text analysis of the documents. The weight of the term depends on its frequency of occurrence in the text and the number of documents it appears in. See, e.g., 23: The weight of a keyword-term is the product of its term frequency and its inverse document frequency. See, e.g., 41 (Table 4.1: A sample interest profile): keywords: 0.7 ukraine nuclear additional returned washington [d] storing said plurality of keywords with regard to said relevance; See, e.g., 56, Table 5.1 (charted above for element 16[b]). See, e.g., 40: Each profile is stored in a different file. It is stored as an ASCII flatfile in a predetermined format. Table 4.1 shows a sample profile. See supra element 16[c]. As explained previously with elements 1[b] and 3, Sheth teaches that the keyword terms (the plurality of keywords) are weighted according to their importance (i.e., relevance). See also Ex Specifically, [t]he weight of the term depends on its frequency of occurrence in the text and the number of documents it appears in. Ex at 23. Keyword vectors are shown in Tables

40 and 5.1, with the keyword terms sorted by weight (i.e., relevance) in descending order with the most relevant terms on top (i.e., prioritized by relative relevance). See Ex. 41, 56; Ex Sheth provides detailed instruction on the mathematical operations for calculating term weights. See Ex at 23; Ex. 1008, 100. With respect to element 16[d], each profile including the keyword vector in every profile is written to a flat ASCII file (i.e., stored). Ex at 40. As explained above and shown in Tables 4.1 and 5.1, each profile contains a plurality of keywords prioritized by their importance (i.e., relevance) to the profile, with weights assigned to each keyword based on its importance, and stored along with the rest of the profile in an ASCII file. Ex at 40, 56; Ex Claim 16 16[e] searching as a background operation storage in at least one computer for documents related to said plurality of stored keywords; Sheth See supra, elements 1[c] and 1[d]. As explained previously, the information filtering module YAIF runs offline, at night (i.e., as a background operation). See, e.g., Ex at 42; Ex ; supra element 1[a]. YAIF searches newsgroup articles (documents) that are stored on USENET servers (storage in at least one computer). See Ex at 42 ( On typical USENET servers, the set of articles in a newsgroup changes on a continual basis. ); Ex YAIF retrieves and scores the newsgroup articles with respect to the user profile. See id. As explained with elements 16[c] and 16[d], -34-

41 each profile contains a keyword vector, and as explained with element 1[d], scoring of a document against a profile includes comparison of their keyword vectors. See also Ex When YAIF searches for high scoring articles to be presented to the user, it searches for documents related to the plurality of stored keywords in the profile. See Ex at 21 ( The advantage of using a common vector space for both documents and queries is that a document can also be used as a query itself i.e., one can find documents that are similar to a given document. ); Ex Claim 16 16[f] determining relevance of a found second document to said plurality of stored keywords; 16[g] conditionally adding a reference to said second document in a results directory. Sheth See supra, elements 1[d], 1[e], and claim 3. See, e.g., 46: While selecting articles, care is taken to prevent presenting the same article twice in different sessions. The profile keeps a list of articles that have been presented to the user during previous sessions, as in the ArtRead list in table 4.1. The top scoring articles are compared to this list to prevent repetitions. See, e.g., 20: Profiles search for articles that are similar to itself. Top-scoring articles are retrieved for presentation to the user. The articles recommended by each of the profiles are collected together and presented to the user. See, e.g., 25-26: There are a number of approaches to selecting the final documents: The number documents contributed by each profile is proportional to its fitness.... The number of documents contributed by each -35-

42 profile is the same.... A third approach is to use a threshold. Any document which scores above the threshold will be selected independent of the profile which scored it.... See supra, element 1[f]. As explained with element 1[f], pointers to recommended articles (i.e., a reference to the second document) are added to the ArtScores directory (the results directory) in the profile. See also Ex Sheth conditionally adds references to articles to the ArtScores directory if the same article has not already been presented to the user. See Ex at 46; Ex Furthermore, Sheth discloses three approaches for selecting which candidate documents will be added to the ArtScores directory and subsequently presented to the user. See Ex at Each approach specifies conditions for adding references to the results directory. Ex Claim The method according to claim 16, with the additional step of comparing the relevance of said second document to a preset threshold. Claim The method according to claim 16, wherein said storage is on a plurality of computers connected to at least one network. Sheth See supra, claim 7. Sheth See supra, claim 12. Claim The method according to claim 16, wherein adding a Sheth See, e.g., 46: While selecting articles, care is taken to prevent presenting the same article twice in different sessions. The profile keeps a list of articles that have been -36-

43 duplicate reference in said results directory is avoided. Claim The method according to claim 16, wherein adding a reference that was previously deleted from said results directory is avoided. presented to the user during previous sessions, as in the ArtRead list in table 4.1. The top scoring articles are compared to this list to prevent repetitions. Sheth See, e.g., 35-36: The user can provide feedback for the articles retrieved by the agent. Under each Agent Icon are small icons bearing the signs '+' and '-' to enable the agent to receive positive and negative feedback respectively. See, e.g., 46: While selecting articles, care is taken to prevent presenting the same article twice in different sessions. The profile keeps a list of articles that have been presented to the user during previous sessions, as in the ArtRead list in table 4.1. The top scoring articles are compared to this list to prevent repetitions. With respect to claim 20, the user can provide negative feedback for articles. Ex at Articles that have received negative feedback will not be kept in i.e., will be deleted from the ArtScores directory (the results directory). Ex Sheth keeps a list of articles that have been presented to the user. Ex at 46. Any reference previously added to the ArtScores directory will not be added again including those previously added and then deleted. Ex To the extent it is not explicitly disclosed, Sheth inherently discloses claim 20. Recommended articles must inherently include a mechanism for deleting results from the ArtScores directory. Ex If Sheth did not have a mechanism for deleting results such as periodically, manually, or automatically once an article has been presented then the ArtScores directory would reach an unmanageable size. Id. Thus, Sheth inherently includes a mechanism for deleting -37-

44 references from the ArtScores directory (i.e., the results directory). Id. Irrespective of how a reference is deleted, Sheth prevents repetitions of that article from being added to the ArtScores (results) directory. See Ex at 46; Ex B. Ground 2: Claim 20 Is Obvious Under 35 U.S.C. 103 by Sheth in View of WebCompass WebCompass was a software tool that found Webpages and automatically generate[d] summaries and keywords for each document it [found] that matches your search criteria. Ex at 6. The user manual ( WebCompass ) discloses remembering when a document has been deleted and not retrieving it again for the same topic. Id. at 19; Ex For the reasons described below, it would have been obvious for a POSITA to combine this functionality, as disclosed in WebCompass, with the information filtering system of Sheth. Ex As explained in Ground 1, Sheth explicitly and inherently discloses claim 20. The combination of Sheth with WebCompass also renders claim 20 obvious. Ex Sheth already kept a list of the articles presented to the user to prevent the same article from being presented twice. See Ex at 46; Ex Thus, it would have been obvious to a POSITA in light of the teachings of WebCompass to add a list of deleted articles to the profiles in Sheth to the extent this functionality was not already present and to compare top-scoring articles to the deleted list before adding them to the results directory. Ex A POSITA would have been motivated to add this feature of WebCompass -38-

45 to Sheth because the references are analogous art in the same field of humancomputer interfaces. Ex Notably, Moukas (the follow-on project to Sheth) cites both Sheth and WebCompass as Related Software Agent Systems. Ex at 25-26; Ex , 41, 112. Additionally, Sheth contains teachings that would have motivated a POSITA to combine it with WebCompass. Sheth teaches that repetitions should be avoided; thus, to the extent that functionality was not already present, a POSITA would have been motivated to add a list of deleted articles as taught by WebCompass to the system in Sheth. Ex Furthermore, the application of WebCompass to Sheth uses a known method to yield a predictable result. Ex WebCompass teaches a known method for avoiding repetition: the system remembers if an article has been deleted. Id. When added to Sheth, the method yields the predictable result that previously-deleted articles are not presented to the user again. Id. Claim The method according to claim 16, wherein adding a reference that was previously deleted from said results directory is avoided. Sheth Sheth: See, e.g., 46: While selecting articles, care is taken to prevent presenting the same article twice in different sessions. The profile keeps a list of articles that have been presented to the user during previous sessions, as in the ArtRead list in table 4.1. The top scoring articles are compared to this list to prevent repetitions. WebCompass: See, e.g., 19: When you delete a document, WebCompass remembers that it has been deleted and will not retrieve it again for the same topic on a subsequent search. -39-

46 C. Ground 3: Claims 1-20 Are Obvious Under 35 U.S.C. 103 by Rucker in View of Sheth Sheth anticipates all claims of the 482 Patent except claim 8 ( wherein said results directory is said first directory ). Rucker discloses that element, but lacks an explicit disclosure regarding certain other elements, such as when searches are performed, or the use of keyword searching. However, Rucker in view of Sheth renders obvious all claims of the 482 Patent. Rucker discloses a Web-page recommendation system called Siteseer that used an individual s bookmarks in particular, the folders and organization of those bookmarks to predict pages of potential interest. See Ex at 1. Rucker treated bookmark folders as a user s personal classification system, and used that information to generate recommendations. Id. Rucker used an almost purely collaborative approach, where its recommendations were based on the bookmarking patterns of other users as opposed to deriv[ing] any semantic value from the contents of the Web pages. See Ex at 2-3; Ex at 113. To provide the user with context, the recommended pages were added back to the bookmark folder that served as the basis for those recommendations. Ex at 3. This usage model, as disclosed in Rucker, was described by the applicant during prosecution of the 482 Patent as an exemplary use-case scenario. Ex at 143 ( As an exemplary use-case scenario, a user browses the web, saving topically-related document links in the same web-favorites folder. Once this -40-

47 precondition is met, the claimed invention software kicks in: deriving keywords from saved documents, thus discerning the topic of interest, then searching for other related documents, resulting in supplementing the directory with newlyfound documents- hence the title of 09/796,235: automatic directory supplementation. ). It would have been obvious to a person of ordinary skill in the art to combine: (a) Rucker s teaching of discerning a user s interests based on the organization of her bookmarks folders, and adding recommendations back into those folders, and (b) Sheth s teaching of creating a profile of user interests and utilizing autonomous software agents to identify similar documents based on keyword analysis. Ex Specifically, it would have been obvious to use a bookmarks folder, as taught by Rucker, as a starting point for generating an interest profile for the user, as taught by Sheth. Id. Sheth then generates recommendations in the same fashion as explained previously with Ground 1. Id. At night, the system iterates through the links and corresponding Web pages in the bookmarks folder similar to the iteration through the ArtFeedback directory of Sheth and generates a profile based on the keywords of those pages. Id. The process for computing the keywords in a document is the same regardless of whether the document is a Webpage or newsgroup articles. Id. Sheth s autonomous agents then search the Web for pages based on their similarity to the user profile, -41-

48 particularly the keyword vector of the profile. Id. Links to pages whose similarity scores exceed a threshold, as taught by Sheth, are added back to the bookmarks folder that started the process, as taught by Rucker. See id.; Ex at 3. A POSITA by at least January 2000 would have easily combined Rucker and Sheth in this way. Ex The combination has a clean demarcation between each reference s functionality: Rucker supplies the input to Sheth s recommendation system, and the output of Sheth is written back to the Favorites folder as taught by Rucker. Id. This minimizes changes to either system. Id. A POSITA would not have to invent any new technology, because by at least January 2000, the techniques disclosed in Rucker and Sheth were well-known in the art and could be implemented using known methods with predictable results. Id. 1. Motivation to Combine As an initial matter, the hypothetical question of whether a POSITA could have or would have found it obvious to combine Rucker and Sheth is answered by the reality that a POSITA in fact combined key elements of the combination described above. Ex In June 1997, Alexandros Moukas, a Ph.D. student in the MIT Media Lab, published a thesis regarding a system called Amalthaea, which was a follow-on project to Sheth. Id.; Ex at 1. At the time, Mr. Moukas would have been a POSITA, having a Masters of Science in Artificial Intelligence and two years of post-secondary education in the MIT Ph.D. -42-

49 program. Ex ; Ex at 1. Mr. Moukas added a number of features to Sheth, including the ability to acquire[] the user s interests by submit[ting] a bookmark list with favorite sites/documents to provide a starting point for the system. Ex at 16; Ex Based on that information, the system will use search engines to find other similar documents. Ex at 16. Like the combination of Rucker and Sheth, Moukas added the ability to use bookmarks to initialize autonomous agents for information filtering. Ex at 31 ( The user submits a list of his favorite bookmarks or documents. This is usually the bookmarks list. Each of the sites in the list is examined and for each site an Information Filtering Agent is created[.] ); Ex Moukas retained the keyword analysis techniques of Sheth, using weighted keyword vectors and TF- IDF to measure the similarity of documents. Ex at 37 ( The basic representation of the Information Filtering Agents and the parsed HTML files is the weighted keyword vector. When the HTML files are processed, a clear-text version is generated, the text is decomposed into its keywords, which are weighted and compose the keyword vector. ); id. at 38 ( Finally, each keyword is weighted by producing its tfidf measure. ); Ex To support the use of Web pages, Moukas also added an engine for retrieving documents from the WWW. Ex at 35; Ex Rucker also teaches that its Siteseer architecture may be enhanced by -43-

50 expanding beyond a purely collaborative approach. Ex at 3 ( While a novel recommendation and categorization system, Siteseer has intrinsic limitations because of its almost purely collaborative approach. ); Ex Rucker explains that its collaborative approach has difficulties creating new categories or recommending new material for which there is no collective experience to leverage. Ex at 3; Ex This is known in the art as the cold start problem. Ex Therefore, such users must first arrive at sites or pages by some other means, such as a search service or editorially built Web directory. Ex at 3. Sheth addresses the problem of discovering new material by providing a serendipity knob for the genetic algorithms of its autonomous agents. Ex at 68 ( [H]ow would the system be able to recommend relevant articles that the user could not possibly have known to ask for in the first place? The genetic algorithm approach provides at least a partial solution to the serendipity problem.... a user can turn the serendipity knob higher, if she really likes to continually receive information about different topics. ); Ex In addition, the keyword analysis of Sheth allows the discovery of new material, does not require collaborative input to the information filtering system, and mitigates the cold start problem. Ex Thus, based on the teachings of Rucker, a POSITA would have been motivated to compliment the collaborative approach of Rucker with the keyword analysis and autonomous agents of Sheth. Id. -44-

51 Sheth also suggests a motivation to combine. Ex Sheth discloses that [t]he amount of user interaction required can be further reduced by use of more intelligent interfaces. Ex at 72. Rucker provides a method for reducing the amount of user interaction required for generating a profile of the user s interests. Ex Rucker teaches that Bookmarks... are a desirable mechanism for gathering preference information as they are already maintained by the user, and thus require no additional behavior for the purpose of informing the recommendation system. Ex at 1. In addition, Rucker and Sheth are analogous art that have been cited together by other references. Ex For example, a 1998 paper by Marko Balabanovic of Stanford regarding recommendation systems cited both Rucker and Sheth 2 on the same page. Ex at 31; see also Ex ; Ex at Rucker and Sheth are both directed to the same field of human-computer interaction, and more specifically, to similar information filtering applications, and they have similar goals of delivering personalized recommendations for online content. See Ex ; Ex at 1 ( Siteseer then delivers personalized recommendations of online content, Web pages, organized according to each 2 The citation was to an IEEE paper by Sheth presenting the same research and system (Newt) that was the subject of his thesis. See Ex. 1011; Ex

52 user s folders. ); Ex at 2 ( This thesis presents the basic framework for personalized information filtering agents, and describes an implementation, Newt, built using the framework. ). It would have been obvious for a POSITA to combine the bookmarking capabilities of Rucker with the interest profiles and autonomous agents of Sheth, because doing so would have merely applied known methods to address the known problem of filtering vast amounts of information into personalized recommendations. Ex The use of bookmarks and Favorites folders for discerning user interests was known in the art of recommendation systems. See id.; Ex at 7; Ex at 6. Using Favorites folders as the starting point for the information filtering system of Sheth would achieve (and did achieve) the predictable result of creating profiles reflecting the keyword content of the Webpages bookmarked in those folders. Ex In addition, the use of autonomous agents and keyword analysis were also known in the art, particularly the term-frequency, inverse-document frequency keyword analysis method used in Sheth, which has used in the field since before the advent of the Internet. See id.; Ex at 23; Ex at 9; Ex at 38. Combining the autonomous agents and keyword analysis of Sheth with the bookmarks-based architecture of Rucker would achieve (and did achieve) the predictable result of retrieving Webpages with similar keyword content to the Webpages bookmarked in the source bookmarks -46-

53 folders. Ex Obviousness Claim 1 1[p]. A computerimplemented method for augmenting a directory without contemporaneous user input comprising: Rucker in view of Sheth Rucker: See, e.g., 1: Siteseer is a Web-page recommendation system that uses an individual s bookmarks and the organization of bookmarks within folders for predicting and recommending relevant pages. See, e.g., 2: See, e.g., 3: Siteseer contextualizes its recommendations by delivering them in the folder served as the basis for the discovery. Thus, in our example, recommendations coming from users in John s Vacation Spots neighborhood will be delivered within the context of his Vacation Spots folder. Sheth: See, e.g., 42: YAIF is a time-intensive process and is run offline. The process is executed every night, so that filtered articles are available to every profile in the morning. Rucker discloses that folders in the user s bookmarks are used to generate -47-